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Update transcribe.py

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Alexander Kuznetsov 2024-11-01 20:27:00 +03:00 committed by GitHub
parent bb8c47519d
commit 80ddd07c28
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1 changed files with 7 additions and 20 deletions
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whisper/transcribe.py
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@ -42,7 +42,7 @@ def transcribe(
verbose: Optional[bool] = None, verbose: Optional[bool] = None,
temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0),
compression_ratio_threshold: Optional[float] = 2.4, compression_ratio_threshold: Optional[float] = 2.4,
compression_ratio_halucination_threshold: Optional[float] = 3, compression_ratio_hallucination_threshold: Optional[float] = 3,
logprob_threshold: Optional[float] = -1.0, logprob_threshold: Optional[float] = -1.0,
no_speech_threshold: Optional[float] = 0.6, no_speech_threshold: Optional[float] = 0.6,
condition_on_previous_text: bool = True, condition_on_previous_text: bool = True,
@ -76,7 +76,7 @@ def transcribe(
compression_ratio_threshold: float compression_ratio_threshold: float
If the gzip compression ratio is above this value, treat as failed If the gzip compression ratio is above this value, treat as failed
compression_ratio_halcination_threshold: float compression_ratio_hallucination_threshold: float
If the gzip compression ratio is above this value after all attempts to decode, treat as a halucination and skip If the gzip compression ratio is above this value after all attempts to decode, treat as a halucination and skip
logprob_threshold: float logprob_threshold: float
@ -106,11 +106,6 @@ def transcribe(
"prompt-engineer" a context for transcription, e.g. custom vocabularies or proper nouns "prompt-engineer" a context for transcription, e.g. custom vocabularies or proper nouns
to make it more likely to predict those word correctly. to make it more likely to predict those word correctly.
carry_initial_prompt: bool
If carry_initial_prompt is True, `initial_prompt` is prepended to the prompt of each internal
`decode()` call. If there is not enough context space at the start of the prompt, it is
left-sliced to make space.
decode_options: dict decode_options: dict
Keyword arguments to construct `DecodingOptions` instances Keyword arguments to construct `DecodingOptions` instances
@ -220,12 +215,14 @@ def transcribe(
): ):
needs_fallback = False # silence needs_fallback = False # silence
if ( if (
compression_ratio_halucination_threshold is not None compression_ratio_hallucination_threshold is not None
and decode_result.compression_ratio > compression_ratio_halucination_threshold and decode_result.compression_ratio > compression_ratio_hallucination_threshold
and t == temperatures[-1] and t == temperatures[-1]
): ):
# Discard the segment # Discard the segment
continue # Skip to the next segment continue # Skip to the next segment
if not needs_fallback: if not needs_fallback:
break break
@ -243,11 +240,9 @@ def transcribe(
all_segments = [] all_segments = []
prompt_reset_since = 0 prompt_reset_since = 0
remaining_prompt_length = model.dims.n_text_ctx // 2 - 1
if initial_prompt is not None: if initial_prompt is not None:
initial_prompt_tokens = tokenizer.encode(" " + initial_prompt.strip()) initial_prompt_tokens = tokenizer.encode(" " + initial_prompt.strip())
all_tokens.extend(initial_prompt_tokens) all_tokens.extend(initial_prompt_tokens)
remaining_prompt_length -= len(initial_prompt_tokens)
else: else:
initial_prompt_tokens = [] initial_prompt_tokens = []
@ -293,13 +288,7 @@ def transcribe(
segment_duration = segment_size * HOP_LENGTH / SAMPLE_RATE segment_duration = segment_size * HOP_LENGTH / SAMPLE_RATE
mel_segment = pad_or_trim(mel_segment, N_FRAMES).to(model.device).to(dtype) mel_segment = pad_or_trim(mel_segment, N_FRAMES).to(model.device).to(dtype)
if carry_initial_prompt:
nignored = max(len(initial_prompt_tokens), prompt_reset_since)
remaining_prompt = all_tokens[nignored:][-remaining_prompt_length:]
decode_options["prompt"] = initial_prompt_tokens + remaining_prompt
else:
decode_options["prompt"] = all_tokens[prompt_reset_since:] decode_options["prompt"] = all_tokens[prompt_reset_since:]
result: DecodingResult = decode_with_fallback(mel_segment) result: DecodingResult = decode_with_fallback(mel_segment)
tokens = torch.tensor(result.tokens) tokens = torch.tensor(result.tokens)
@ -553,8 +542,6 @@ def cli():
parser.add_argument("--suppress_tokens", type=str, default="-1", help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations") parser.add_argument("--suppress_tokens", type=str, default="-1", help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations")
parser.add_argument("--initial_prompt", type=str, default=None, help="optional text to provide as a prompt for the first window.") parser.add_argument("--initial_prompt", type=str, default=None, help="optional text to provide as a prompt for the first window.")
parser.add_argument("--carry_initial_prompt", type=str2bool, default=False, help="if True, prepend initial_prompt to every internal decode() call. May reduce the effectiveness of condition_on_previous_text")
parser.add_argument("--condition_on_previous_text", type=str2bool, default=True, help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop") parser.add_argument("--condition_on_previous_text", type=str2bool, default=True, help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop")
parser.add_argument("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default") parser.add_argument("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default")